Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/stefan-stepzero/shipkit/shipkit-semantic-qanpx skills add stefan-stepzero/shipkit --skill shipkit-semantic-qagit clone --depth 1 https://github.com/stefan-stepzero/shipkitWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00078 | $0.07344 |
| Opus 5 | $0.00039 | $0.03672 |
| Sonnet 5 | $0.00016 | $0.01469 |
| Haiku 4.5 | $0.00008 | $0.00734 |
Grade A, and why
shipkit-semantic-qa scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 672 lines — stays where its author put it; the contents beside it link to each section on GitHub.
shipkit-semantic-qa - Semantic Quality Assurance
Purpose: Define test inputs and quality criteria, generate test scripts, run them, and let Claude semantically judge outputs (API responses or UI screenshots) against human-defined criteria.
Pattern: One skill, one loop — Setup → Run → Judge. Two suite types: backend (API/LLM pipeline) and frontend (visual components).
When to Invoke
User triggers:
- "Semantic QA", "Set up QA", "Quality check"
- "Visual QA", "Screenshot QA", "Check my UI"
- "Judge outputs", "Run QA suite", "Check quality"
- "Set up quality criteria", "Define test inputs"
Workflow position:
- After features are implemented (something to test)
- Before verify/preflight (catches quality issues early)
- Can run standalone against any API or UI
Prerequisites
Required: None (Setup mode creates everything)
Helpful:
.shipkit/stack.json— Tech stack informs script generation.shipkit/specs/— Acceptance criteria can seed quality criteria- Playwright installed (for frontend suites only)
Process
Completion Tracking
In --full mode (all 3 phases sequential), create tasks at the start:
TaskCreate: "Setup: Define criteria + generate test script"TaskCreate: "Run: Execute tests + verify output count"TaskCreate: "Judge: Evaluate ALL outputs against ALL criteria"TaskCreate: "Write judgment.md + judgment.json"
TaskUpdate each task to in_progress when starting it, completed when done.
In Judge mode with 5+ outputs, create one task per output to prevent partial evaluation. Do NOT present judgment summary until all output evaluations are complete and files are written.
Step 0: Mode Detection
Determine mode from arguments and state:
Explicit flag provided:
--setup→ Setup mode--run→ Run mode--judge→ Judge mode--full→ Setup (if needed) → Run → Judge--fidelity→ Fidelity mode — score a built app against captured intent (completeness + essence), emit a fidelity scorecard. See Step 5. Add a second suite/arm to compare two builds under one rubric.
What ships with it
19 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/criteria-guide.md 4.1 KB
- references/example.json 4.8 KB
- references/fidelity-scorecard-schema.md 24 KB
- references/output-schema.md 2.9 KB
- references/README.md 757 B
- tools/fidelity/_common.py 2.5 KB runs code
- tools/fidelity/_declared.py 15 KB runs code
- tools/fidelity/_smoke/coach/leaderboard.py 293 B runs code
- tools/fidelity/_smoke/ExecView.tsx 298 B
- tools/fidelity/_smoke/pages/index.tsx 311 B
- tools/fidelity/_smoke/PredictivePacing.tsx 600 B
- tools/fidelity/_smoke/scratch/ExperimentPanel.tsx 724 B
- tools/fidelity/_smoke/spec.json 2.9 KB
- tools/fidelity/_smoke/StaffDashboard.tsx 466 B
- tools/fidelity/fidelity-score.py 30 KB runs code
- tools/fidelity/mock-seam-detector.py 11 KB runs code
- tools/fidelity/README.md 8.6 KB
- tools/fidelity/ssot-checker.py 6.8 KB runs code
- tools/fidelity/unbacked-surface-checker.py 6.3 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 672 lines · 78 tokens per session scan A 0ef6a87d42e7
shipkit-semantic-qa is a skill published in the GitHub repository stefan-stepzero/shipkit (1 stars, last pushed 1mo ago), licensed MIT. It adds 78 tokens to every session and 7,344 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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